The present disclosure involves systems, software, and computer-implemented methods for automatically predicting a probability of success or failure of change requests for an organization's information technology (IT) infrastructure or use environment. One example system includes at least one repository storing information corresponding to one or more prior change requests, at least one memory storing instructions and at least one prediction tool, and at least one hardware processor. Each change request corresponds to a request for a modification to one or more sections of the infrastructure. The instructions instruct the at least one hardware processor to receive one or more parameters corresponding to a new change request associated with the infrastructure. The parameters are provided as input to the at least one prediction tool, and in response, a probability of success of the new change request is received as an output of the at least one prediction tool.
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6. The system of claim 1, wherein the output of the at least one prediction tool further comprises one or more recommendations for modifying the new change request.
7. The system of claim 6, wherein the one or more recommendations for modifying the new change request comprises recommendations for modifying at least one of the one or more parameters, or modifying one or more tasks to be performed to effect the requested modification associated with the new change request.
14. The system of claim 1, wherein the at least one prediction tool includes a machine learning algorithm that is one of an artificial neural network, or a decision tree model having one or more of feature importance or gradient boosting.
17. The non-transitory computer-readable medium of claim 15, wherein the at least one prediction tool includes a machine learning algorithm that is one of an artificial neural network, or a decision tree model having one or more of feature importance or gradient boosting.
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October 3, 2019
November 22, 2022
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